DeepScaleR-1.5B-Preview
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Deepscaler 1.5B Preview
Overview :
DeepScaleR-1.5B-Preview is a large language model optimized by reinforcement learning, dedicated to enhancing the capabilities of solving mathematical problems. It achieves significant improvements in accuracy within long-text inference scenarios, driven by distributed reinforcement learning algorithms. Key advantages include efficient training strategies, notable performance gains, and the flexibility of open-source availability. Developed by the Sky Computing Lab and Berkeley AI Research team at the University of California, Berkeley, this model aims to advance the application of artificial intelligence in education, especially in mathematics education and competitive mathematics. Available under the MIT open-source license, it is completely free for researchers and developers to use.
Target Users :
This model is primarily aimed at researchers, developers, and math competition participants in the educational field. Researchers can leverage its open-source capabilities for algorithm exploration and enhancement; developers can integrate it into educational software to provide intelligent tutoring for students; and math competition participants can utilize the model for problem-solving practice and idea generation.
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Use Cases
Integrate this model into math competition tutoring software to provide real-time problem-solving suggestions and insights for students.
Researchers exploring new optimization methods for reinforcement learning algorithms utilizing the model's open-source code.
Math teachers using the model to generate practice problems and solutions to enhance classroom instruction.
Features
Optimizes model performance using distributed reinforcement learning algorithms
Supports long text contexts (up to 24K tokens), enhancing the ability to solve complex problems
Trained on a large-scale dataset of mathematical problems, covering competition questions like AIME and AMC
Provides efficient inference service support, compatible with various high-performance inference systems
Open-source model architecture and training methods, facilitating further development and research by developers
How to Use
1. Visit the Hugging Face website and download the DeepScaleR-1.5B-Preview model files.
2. Install a supported inference system (such as vLLM or Hugging Face Text Generation Inference).
3. Load the model into the inference system, configuring appropriate parameters (such as context length, sampling strategy, etc.).
4. Use the model to infer and solve math problems, calling the model's service via API.
5. Parse and process the model's output according to actual needs, such as extracting answers or generating solution steps.
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